A family of model predictive control algorithms with artificial neural networks

• Autorzy: Ławryńczuk M.
• Języki publikacji: EN

Abstrakty

EN

This paper details nonlinear Model-based Predictive Control (MPC) algorithms for MIMO processes modelled by means of neural networks of a feedforward structure. Two general MPC techniques are considered: the one with Nonlinear Optimisation (MPC-NO) and the one with Nonlinear Prediction and Linearisation (MPC-NPL). In the first case a nonlinear optimisation problem is solved in real time on-line. In order to reduce the computational burden, in the second case a neural model of the process is used on-line to determine local linearisation and a nonlinear free trajectory. Single-point and multi-point linearisation methods are discussed. The MPC-NPL structure is far more reliable and less computationally demanding in comparison with the MPC-NO one because it solves a quadratic programming problem, which can be done efficiently within a foreseeable time frame. At the same time, closed-loop performance of both algorithm classes is similar. Finally, a hybrid MPC algorithm with Nonlinear Prediction, Linearisation and Nonlinear optimisation (MPC-NPL-NO) is discussed.

Słowa kluczowe

EN

predictive control; neural networks; optimisation; linearisation; quadratic programming

PL

sterowanie predykcyjne; sieć neuronowa; optymalizacja; linearyzacja; programowanie kwadratowe

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2007
• Tom: Vol. 17, no 2
• Strony: 217--232
• Opis fizyczny: Bibliogr. 42 poz., rys., wykr.

Twórcy

autor

Ławryńczuk M.

• Institute of Control and Computation Engineering Faculty of Electronics and Information Technology Warsaw University of Technology ul. Nowowiejska 15/19, 00–665 Warsaw, Poland,

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